This lecture discusses about the analysis of randomized algorithms, a randomized quick-sort example is discussed.
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What is DETERMINISTIC ENCRYPTION? What does DETERMINISTIC ENCRYPTION mean? DETERMINISTIC ENCRYPTION meaning - DETERMINISTIC ENCRYPTION definition - DETERMINISTIC ENCRYPTION explanation.
Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license.
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A deterministic encryption scheme (as opposed to a probabilistic encryption scheme) is a cryptosystem which always produces the same ciphertext for a given plaintext and key, even over separate executions of the encryption algorithm. Examples of deterministic encryption algorithms include RSA cryptosystem (without encryption padding), and many block ciphers when used in ECB mode or with a constant initialization vector.
Deterministic encryption can leak information to an eavesdropper, who may recognize known ciphertexts. For example, when an adversary learns that a given ciphertext corresponds to some interesting message, they can learn something every time that ciphertext is transmitted. To gain information about the meaning of various ciphertexts, an adversary might perform a statistical analysis of messages transmitted over an encrypted channel, or attempt to correlate ciphertexts with observed actions (e.g., noting that a given ciphertext is always received immediately before a submarine dive). This concern is particularly serious in the case of public key cryptography, where any party can encrypt chosen messages using a public encryption key. In this case, the adversary can build a large "dictionary" of useful plaintext/ciphertext pairs, then observe the encrypted channel for matching ciphertexts.
While deterministic encryption schemes can never be semantically secure, they have some advantages over probabilistic schemes.
One primary motivation for the use of deterministic encryption is the efficient searching of encrypted data. Suppose a client wants to outsource a database to a possibly untrusted database service provider. If each entry is encrypted using a public-key cryptosystem, anyone can add to the database, and only the distinguished "receiver" who has the private key can decrypt the database entries. If, however, the receiver wants to search for a specific record in the database, this becomes very difficult. There are some Public Key encryption schemes that allow keyword search, however these schemes all require search time linear in the database size. If the database entries were encrypted with a deterministic scheme and sorted, then a specific field of the database could be retrieved in logarithmic time.
Assuming that a deterministic encryption scheme is going to be used, it is important to understand what is the maximum level of security that can be guaranteed.
A number of works have focused on this exact problem. The first work to rigorously define security for a deterministic scheme was in CRYPTO 2007. This work provided fairly strong security definitions (although weaker than semantic security), and gave constructions in the random oracle model. Two follow-up works appeared the next year in CRYPTO 2008, giving definitional equivalences and constructions without random oracles , .
To counter this problem, cryptographers proposed the notion of "randomized" or probabilistic encryption. Under these schemes, a given plaintext can encrypt to one of a very large set of possible ciphertexts, chosen randomly during the encryption process. Under sufficiently strong security guarantees the attacks proposed above become infeasible, as the adversary will be unable to correlate any two encryptions of the same message, or correlate a message to its ciphertext, even given access to the public encryption key. This guarantee is known as semantic security or indistinguishability, and has several definitions depending on the assumed capabilities of the attacker.

Optimal Stopping. The Secretary Problem Explained. Taken from a chapter of the book "Algorithms to Live By - The Computer Science of Human Decisions" by Brian Christian and Tom Griffiths
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Machine Learning Explained
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https://www.amazon.com/Algorithms-Live-Computer-Science-Decisions/dp/1627790365
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Hi, everyone. You are very welcome to week two of our NLP course. And this week is about very core NLP tasks. So we are going to speak about language models first, and then about some models that work with sequences of words, for example, part-of-speech tagging or named-entity recognition. All those tasks are building blocks for NLP applications. And they're very, very useful. So first thing's first. Let's start with language models. Imagine you see some beginning of a sentence, like This is the. How would you continue it? Probably, as a human,you know that This is how sounds nice, or This is did sounds not nice. You have some intuition. So how do you know this? Well, you have written books. You have seen some texts. So that's obvious for you. Can I build similar intuition for computers? Well, we can try. So we can try to estimate probabilities of the next words, given the previous words. But to do this, first of all,we need some data. So let us get some toy corpus. This is a nice toy corpus about the house that Jack built. And let us try to use it to estimate the probability of house, given This is the. So there are four interesting fragments here. And only one of them is exactly what we need. This is the house. So it means that the probability will be one 1 of 4. By c here, I denote the count. So this the count of This is the house,or any other pieces of text. And these pieces of text are n-grams. n-gram is a sequence of n words. So we can speak about 4-grams here. We can also speak about unigrams, bigrams, trigrams, etc. And we can try to choose the best n,and we will speak about it later. But for now, what about bigrams? Can you imagine what happens for bigrams, for example, how to estimate probability of Jack,given built? Okay, so we can count all different bigrams here, like that Jack, that lay, etc., and say that only four of them are that Jack. It means that the probability should be 4 divided by 10. So what's next? We can count some probabilities. We can estimate them from data. Well, why do we need this? How can we use this? Actually, we need this everywhere. So to begin with,let's discuss this Smart Reply technology. This is a technology by Google. You can get some email, and it tries to suggest some automatic reply. So for example, it can suggest that you should say thank you. How does this happen? Well, this is some text generation, right? This is some language model. And we will speak about this later,in many, many details, during week four. So also, there are some other applications, like machine translation or speech recognition. In all of these applications, you try to generate some text from some other data. It means that you want to evaluate probabilities of text, probabilities of long sequences. Like here, can we evaluate the probability of This is the house, or the probability of a long,long sequence of 100 words? Well, it can be complicated because maybe the whole sequence never occurs in the data. So we can count something, but we need somehow to deal with small pieces of this sequence, right? So let's do some math to understand how to deal with small pieces of this sequence. So here, this is our sequence of keywords. And we would like to estimate this probability. And we can apply chain rule,which means that we take the probability of the first word, and then condition the next word on this word, and so on. So that's already better. But what about this last term here? It's still kind of complicated because the prefix, the condition, there is too long. So can we get rid of it? Yes, we can. So actually, Markov assumption says you shouldn't care about all the history. You should just forget it. You should just take the last n terms and condition on them, or to be correct, last n-1 terms. So this is where they introduce assumption, because not everything in the text is connected. And this is definitely very helpful for us because now we have some chance to estimate these probabilities. So here, what happens for n = 2, for bigram model? You can recognize that we already know how to estimate all those small probabilities in the right-hand side,which means we can solve our task. So for a toy corpus again,we can estimate the probabilities. And that's what we get. Is it clear for now? I hope it is. But I want you to think about if everything is nice here. Are we done?

There are two prices that are critical for any investor to know: the current price of the investment he or she owns, or plans to own, and its future selling price. Despite this, investors are constantly reviewing past pricing history and using it to influence their future investment decisions. Some investors won't buy a stock or index that has risen too sharply, because they assume that it's due for a correction, while other investors avoid a falling stock, because they fear that it will continue to deteriorate. http://www.garguniversity.com Check out Ebook "Mind Math" from Dr. Garg
https://www.amazon.com/MIND-MATH-Learn-Math-Fun-ebook/dp/B017QEIF18

Ever wondered how I consume research so fast? I'm going to describe the process i use to read lots of machine learning research papers fast and efficiently. It's basically a 3-pass approach, i'll go over the details and show you the extra resources I use to learn these advanced topics. You don't have to be a PhD, anyone can read research papers. It just takes practice and patience.
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https://www.quora.com/How-do-I-start-reading-research-papers-on-Machine-Learning
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Check my previous video on simpler primality testing algorithms:
https://www.youtube.com/watch?v=LULHTKznczA
Check my previous video on how powmod can be built in polynomial time:
https://www.youtube.com/watch?v=qed48E92qXc
Fermat's primality test: https://en.wikipedia.org/wiki/Fermat_primality_test
Carmichael numbers: https://en.wikipedia.org/wiki/Carmichael_number
Soup's number theory book: http://www.shoup.net/ntb/ntb-v2.pdf
This is an implementation of Fermat's primality test in Python. The algorithm is polynomial in the size of the input and it works in O(k polylog(p)) where k is the test accuracy parameter and p is the candidate being tested.
Because Fermat's algorithm uses randomness, it is a probabilistic algorithm.
Fermat's test is a flawed algorithm because Carmichael numbers are composite numbers that the test thinks are prime.
Polynomial-time primality testing has many applications in cryptography such as key generation.
If you liked this video, please thumbs up and subscribe. This is one of my first videos. Please leave feedback about what you think I can improve and what other topics you would like to see.
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The Miller-Rabin primality test: https://en.wikipedia.org/wiki/Miller%E2%80%93Rabin_primality_test
A python implementation of the probabilistic Miller-Rabin primality test. This test runs in polynomial time O(k polylog(p)) and has a negligible probability of failure 4^(-k), making it suitable for cryptographic applications.
This video builds up from Fermat's primality test. Watch my previous video where I explain it: https://www.youtube.com/watch?v=qDakpCEW5-0
Euclid's lemma: https://en.wikipedia.org/wiki/Euclid%27s_lemma
Fermat's little theorem: https://en.wikipedia.org/wiki/Fermat%27s_little_theorem
Soup's number theory book: http://www.shoup.net/ntb/ntb-v2.pdf
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In 2012, scientists developed a system to predict what number a rolled die would land on. Is anything truly random or is it all predictable?
Can Game Theory Help A Presidential Candidate Win? - http://bit.ly/2bMqILU
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On Fair And Randomness
http://www.sciencedirect.com/science/article/pii/S0890540109001369
"We investigate the relation between the behavior of non-deterministic systems under fairness constraints, and the behavior of probabilistic systems. To this end, first a framework based on computable stopping strategies is developed that provides a common foundation for describing both fair and probabilistic behavior. On the basis of stopping strategies it is then shown that fair behavior corresponds in a precise sense to random behavior in the sense of Martin-Löf's definition of randomness."
Predicting A Die Throw
http://phys.org/news/2012-09-die.html
"Vegas, Monte Carlo, and Atlantic City draw people from around the world who are willing to throw the dice and take their chances. Researchers from the Technical University of Lodz, Poland, have spotted something predictable in the seemingly random throw of the dice."
HTG Explains: How Computers Generate Random Numbers
http://www.howtogeek.com/183051/htg-explains-how-computers-generate-random-numbers/
"Computers generate random number for everything from cryptography to video games and gambling. There are two categories of random numbers - "true" random numbers and pseudorandom numbers - and the difference is important for the security of encryption systems."
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Lecture title: "The Growth of Cryptography"
Ronald L. Rivest, a professor of electrical engineering and computer science who helped develop one of the world's most widely used Internet security systems, was MIT’s James R. Killian, Jr. Faculty Achievement Award winner for 2010–2011. Rivest, the Andrew and Erna Viterbi professor in MIT's Department of Electrical Engineering and Computer Science, is known for his pioneering work in the field of cryptography, computer, and network security.
February 8, 2011
Huntington Hall (10-250)

This talk discards hand-wavy pop-science metaphors and answers a simple question: from a computer science perspective, how can a quantum computer outperform a classical computer? Attendees will learn the following:
- Representing computation with basic linear algebra (matrices and vectors)
- The computational workings of qbits, superposition, and quantum logic gates
- Solving the Deutsch oracle problem: the simplest problem where a quantum computer outperforms classical methods
- Bonus topics: quantum entanglement and teleportation
The talk concludes with a live demonstration of quantum entanglement on a real-world quantum computer, and a demo of the Deutsch oracle problem implemented in Q# with the Microsoft Quantum Development Kit. This talk assumes no prerequisite knowledge, although comfort with basic linear algebra (matrices, vectors, matrix multiplication) will ease understanding.
See more at https://www.microsoft.com/en-us/research/video/quantum-computing-computer-scientists/

Turing Machines are the basis of modern computing, but what actually is a Turing Machine? Assistant Professor Mark Jago explains.
Turing & The Halting Problem: http://youtu.be/macM_MtS_w4
Busy Beavers: https://youtu.be/CE8UhcyJS0I
Avatars & In-Flight VR: http://youtu.be/TLKqKlrQv4s
The (pink) VR Simulator: http://youtu.be/Lm0lA0enPSk
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Michael A. Osborne is an information engineer; more specifically, he works in Machine Learning (a component of Artificial Intelligence). Professor Osborne designs intelligent systems: algorithms capable of substituting for human time and attention. Such algorithms, like humans, are faced with the task of understanding and acting upon complex, uncertain, data. Professor Osborne is also interested in more applied problems related to sensor networks, including fault and changepoint detection, automated observation selection and sensor placement. He has also applied probabilistic techniques in a variety of interdisciplinary collaborations, ranging from autonomous vehicles to user interfaces, astrostatistics to zoology.
At the "digitising europe" summit Michael kicked off the first session with a keynote speech on 'Computerisation and the Reinvention of Work'.
More:
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High frequency trading is a form of algorithm trading that uses powerful computers to transact a large number of orders at very fast speeds.
The number of competitors in the market is very limited: only international banks, which already have great influence in the world of finance, are present many questions arise about the fairness of banks and trading players fa cing this technology. Do these methods help to form a two-speed market and two "classes" of traders?
Our product is an innovative program which will be able to help professionals, retails and organisms trading on a market and that although the dominant presence of high frequency trading. In fact, our program deals with milliseconds data in order to help the user to see precise market indicators, in real time. The main difference between existing technologies and our prototype is first the analysis in milliseconds and then the flexibility and adaptability: according to the market data, our system will calculate indicators; the user is free to select the one he wants.
So our system is not just the implementation of the statistical and probabilistic laws applied to an order book. Our system is a real decision support: according the market data, our system shows the different indicators and proposes the best time to buy or sell.
Find our project on : https://github.com/NicolasGriere/High-Frequency-Data-Order-Book-Analyser

This is the third lecture of the course ICS 444: Computer & Network Security offered at King Fahd University of Petroleum and Minerals (KFUPM). This lecture was presented in the Spring 2016 semester (KFUPM term: 152).
This course is an introduction to computer and network security; Security services: confidentiality, integrity, availability, accountability; Hacker techniques and attack types; Public and private key encryption; Authentication; Digital signature; User identification and access control; Computer viruses, Trojans and worms; Risk management and analysis; Information security process; Internet security: security protocols such as IPSec, SSL, TLS, email and web security; Security technologies and systems: Firewalls, VPN and IDS.
The course is offered by Dr. Sami Zhioua. He is an Assistant Professor at the Information and Computer Science Department (ICS) at King Fahd University of Petroleum and Minerals (KFUPM). Learn more about Dr. Zhioua here: http://faculty.kfupm.edu.sa/ICS/zhioua/
Recorded on: 04/02/2016

Cybersecurity is a set of techniques to protect the secrecy, integrity, and availability of computer systems and data against threats. In today’s episode, we’re going to unpack these three goals and talk through some strategies we use like passwords, biometrics, and access privileges to keep our information as secure, but also as accessible as possible. From massive Denial of Service, or DDos attacks, to malware and brute force password cracking there are a lot of ways for hackers to gain access to your data, so we’ll also discuss some strategies like creating strong passwords, and using 2-factor authentication, to keep your information safe.
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Over the last two decades, trading has seen a remarkable evolution from open-outcry in the Wall Street pits to screen trading all the way to current automation and high-frequency trading (HFT). The success of machine learning and artificial intelligence (AI) seems like natural progression for the evolution of trading. However, unlike other fields of AI, trading has some domain specific problems that project the dream of set-it-and-forget-it money making machines still some way in the future. This talk will describe the current challenges for intelligent autonomous trading systems and provides some practical examples where machine learning is already being used in financial applications.
Details: https://confengine.com/odsc-india-2018/proposal/6892/intelligent-autonomous-trading-systems-are-we-there-yet
Conference: https://india.odsc.com/

To get this project in ONLINE or through TRAINING Sessions, Contact:JP INFOTECH, Old No.31, New No.86, 1st Floor, 1st Avenue, Ashok Pillar, Chennai -83.
Landmark: Next to Kotak Mahendra Bank.
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Landmark: Next to VVP Nagar Arch.
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Product Aspect Ranking and Its Applications
Numerous consumer reviews of products are now available on the Internet. Consumer reviews contain rich and valuable knowledge for both firms and users. However, the reviews are often disorganized, leading to difficulties in information navigation and knowledge acquisition. This article proposes a product aspect ranking framework, which automatically identifies the important aspects of products from online consumer reviews, aiming at improving the usability of the numerous reviews. The important product aspects are identified based on two observations: 1) the important aspects are usually commented on by a large number of consumers and 2) consumer opinions on the important aspects greatly influence their overall opinions on the product. In particular, given the consumer reviews of a product, we first identify product aspects by a shallow dependency parser and determine consumer opinions on these aspects via a sentiment classifier. We then develop a probabilistic aspect ranking algorithm to infer the importance of aspects by simultaneously considering aspect frequency and the influence of consumer opinions given to each aspect over their overall opinions. The experimental results on a review corpus of 21 popular products in eight domains demonstrate the effectiveness of the proposed approach. Moreover, we apply product aspect ranking to two real-world applications, i.e., document-level sentiment classification and extractive review summarization, and achieve significant performance improvements, which demonstrate the capacity of product aspect ranking in facilitating real-world applications.

Jintai Ding of the University of Cincinnati and the Chinese Academy of Sciences presented a talk titled: ZHFE, a new multivariate public key encryption scheme at the 2014 PQCrypto conference in October, 2014.
Abstract: In this paper we propose a new multivariate public key encryption scheme named ZHFE. The public key is constructed using as core map two high rank HFE polynomials. The inversion of the public key is performed using a low degree polynomial of Hamming weight three. This low degree polynomial is obtained from the two high rank HFE polynomials, by means of a special reduction method that uses HFE polynomials. We show that ZHFE is relatively efficient and the it is secure against the main attacks that have threatened the security of HFE. We also propose parameters for a practical implementation of ZHFE.
PQCrypto
2014 Book: http://www.springer.com/computer/security+and+cryptology/book/978-3-319-11658-7
Workshop: https://pqcrypto2014.uwaterloo.ca/
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Bio
Professor Andrew Blake is Director of the Alan Turing Institute. Prior to joining the institute in 2015, Professor Blake held the position of Microsoft Distinguished Scientist and Laboratory Director of Microsoft Research Cambridge, England. He joined Microsoft in 1999 as a Senior Researcher to found the Computer Vision group. In 2008 he became a Deputy Managing Director at the lab, before assuming the directorship in 2010. Before joining Microsoft Andrew trained in mathematics and electrical engineering in Cambridge England, and studied for a doctorate in Artificial Intelligence in Edinburgh. He was an academic for 18 years, latterly on the faculty at Oxford University, where he was a pioneer in the development of the theory and algorithms that can make it possible for computers to behave as seeing machines.
Professor Blake has published several books including “Visual Reconstruction” with A.Zisserman (MIT press), “Active Vision” with A. Yuille (MIT Press) and “Active Contours” with M. Isard(Springer-Verlag). He has twice won the prize of the European Conference on Computer Vision, with R. Cipolla in 1992 and with M. Isard in 1996, and was awarded the IEEE David Marr Prize (jointly with K. Toyama) in 2001.
In 2006 the Royal Academy of Engineering awarded him its Silver Medal and in 2007 the Institution of Engineering and Technology presented him with the Mountbatten Medal(previously awarded to computer pioneers Maurice Wilkes and Tim Berners-Lee, amongst others.) He was elected Fellow of the Royal Academy of Engineering in 1998, Fellow of the IEEE in 2008, and Fellow of the Royal Society in 2005. In 2010, Andrew was elected to the council of the Royal Society. In 2011, he and colleagues at Microsoft Research received the Royal Academy of EngineeringMacRobert Award for their machine learning contribution to Microsoft Kinect human motion-capture. In 2012 Andrew was elected to the board of the EPSRC and also received an honorary degree of Doctor of Science from the University of Edinburgh. In 2013 Andrew was awarded an honorary degree of Doctor of Engineering from the University of Sheffield. In 2014, Andrew gave the prestigious Gibbs lecture at the Joint Mathematics Meetings (transcript available here). Professor Andrew Blake has been named as the recipient of the 2016 BCS Lovelace Medal, the top award in computing in the UK, awarded by BCS, The Chartered Institute for IT. The award is presented annually to individuals who, in the opinion of BCS, have made a significant contribution to the advancement of Information Systems.
#TuringShortTalks

Frank Thomson (Tom) Leighton PhD ’81
Professor of Applied Mathematics
Head, Algorithms Group (CSAIL)
Professor Tom Leighton is a professor of applied mathematics at MIT who has served as the head of the Algorithms Group in MIT’s Computer Science and Artificial Intelligence Lab (CSAIL) since its inception in 1996. In 1998, Professor Leighton co-founded Akamai Technologies, the world’s leading Internet content delivery network, where he currently serves as chief scientist and is a member of the board of directors. He is a preeminent authority on algorithms for network applications, and has published on cryptography, parallel architectures, and distributed computing, among other topics. Professor Leighton is a fellow of the American Academy of Arts & Sciences, a fellow of the Society for Industrial and Applied Mathematics, and a member of the National Academy of Engineering and National Academy of Sciences. He holds a BS in engineering from Princeton University and a PhD in mathematics from MIT.

How many different birthdays on average will a group of n randomly selected people have ?
P.S. I`d like to thank the guy nicknamed Madman for his help in creating this video.
Feel free to comment, like/dislike, subscribe.
http://en.wikipedia.org/wiki/Birthday_problem

This video is part of the Infosec Video Collection at SecurityTube.net: http://www.securitytube.net
Shmoocon 2012:
Peter Gutmann is a researcher in the Department of Computer Science at the University of Auckland working on design and analysis of cryptographic security architectures and security usability. He helped write the popular PGP encryption package, has authored a number of papers and RFC's on security and encryption, and is the author of the open source cryptlib security toolkit and an upcoming book on security engineering. In his spare time he pokes holes in whatever security systems and mechanisms catch his attention and grumbles about the lack of consideration of human factors in designing security systems.

Bonus Features: http://www.hiddensecretsofmoney.com Today, mankind stands at a crossroads, and the path that humanity chooses may have a greater impact on our freedom and prosperity than any event in history. In 2008 a new technology was introduced that is so important that its destiny, and the destiny of mankind are inextricably linked. It is so powerful that if captured and controlled, it could enslave all of humanity. But if allowed to remain free and flourish - it could foster unimaginable levels of peace and prosperity. It has the power to replace all financial systems globally, to supplant ninety percent of Wall St, and to provide some functions of government. It has no agenda. It's always fair and impartial. It can not be manipulated, subverted, corrupted or cheated. And - it inverts the power structure and places control of one's destiny in the hands of the individual. In the future, when we look back at the 2.6 million-year timeline of human development and the major turning points that led to modern civilization - the creation of farming, the domestication of animals, the invention of the wheel, the harnessing of electricity and the splitting of the atom - the sixty year development of computers, the internet and this new technology will be looked upon as a single event...a turning point that will change the course of human history. It's called Full Consensus Distibuted Ledger Technology, and so far its major use has been for cryptocurrencies such as Bitcoin....but its potential goes far, far beyond that.
The Crypto Revolution: From Bitcoin to Hashgraph is our latest episode of Hidden Secrets of Money. It’s about the evolution of cryptocurrencies and full consensus distributed ledger technology, and how they will change our world. I believe that this video is by far the easiest way for the average person to gain an understanding of what cryptocurrencies are and how they work, but more importantly, the immense power of full consensus distributed ledger technology and the impact it will have on our daily lives.
I have an absolute passion for monetary history and economics, and I love teaching them. Cryptocurrencies are our future, and there is no escaping it… this is the way everything will be done from now on. But, we now stand at a crucial turning point in history. Full consensus ledgers such as Blockchain and Hashgraph have the power to enslave us, or free us… it all depends on how we choose to use them. If we choose to support centralized versions issued by governments and the financial sector we will be granting them more control over our daily lives. Politicians and bureaucrats will be able raise taxes instantly, whenever they want, on every dollar you make as you make them, and every dollar you spend as you spend them. If they think the economy needs stimulating they'll be able to enforce huge negative interest rates, effectively punishing you for not spending everything you earn before you earn it. They'll be able to decide where you can go and where you can’t, what you can buy and what you can’t, and what you can do and whatever they decide you can’t do… and if they don't like you, they can just disconnect you from the monetary system.
So, will the monetary system become fully distributed and help to free mankind, or will it be centralized and enslave us? The choice is in front of us right now, and our decisions will create our future. I believe that this will be a binary outcome, there is no middle ground, it will either be one future or the other. The question is, will it be the future we want? Or the future they want?
I’m a precious metals dealer and one thing I’ve learned is that gold, silver, and now free market decentralized cryptocurrencies, represent freedom. Because of this knowledge I started investing in crypto currencies long ago and also became one of the first precious metals dealers to accept bitcoin as payment for gold and silver.
I would really appreciate it if you could share this video with everyone you know. I think it’s very important that as many people as possible find out about the changes to the global monetary system that are happening right now… nothing will affect us more, and everyone’s future depends on it.
Thanks, Mike
If you enjoyed watching this video, be sure to pick up a free copy of Mike's bestselling book, Guide to Investing in Gold & Silver: https://goldsilver.com/buy-online/investing-in-gold-and-silver/
(Want to contribute closed captions in your language for our videos? Visit this link: http://www.youtube.com/timedtext_cs_panel?tab=2&c=UCThv5tYUVaG4ZPA3p6EXZbQ)

International Journal in Foundations of Computer Science & Technology (IJFCST)
ISSN : 1839-7662
http://wireilla.com/ijfcst/index.html
Scope & Topics
Over the last decade, there has been an explosion in the field of computer science to solve various problems from mathematics to engineering. This journal aims to provide a platform for exchanging ideas in new emerging trends that needs more focus and exposure and will attempt to publish proposals that strengthen our goals. Topics of interest include, but are not limited to the following:
• Algorithms
• Automata and Formal Languages
• Novel Data structures
• Combinatorial Games
• Computational Complexity
• Programming Languages
• Computational Number Theory
• Cryptography
• Database Theory
• Queuing Methods
• Distributed & High Performance Computing
• Computer Security
• Program Semantics and Logic
• Probabilistic Methods
• Computation Biology
• Internet & Cloud Computing
• Software Engineering
• Artificial Intelligence
• Biochemistry
• Astrophysics
• Geometric Modeling, Graphics and Visualization
• Other Emerging applications
Paper Submission
Authors are invited to submit papers for this journal through E-mail: [email protected] Submissions must be original and should not have been published previously or be under consideration for publication while being evaluated for this Journal.
For other details please visit : http://wireilla.com/ijfcst/index.html

What is the effect of voluntary burning or accidental loss? Does it impact deflation a lot? Why is there a limit for 21 million bitcoin? Why are there 8 decimal places? What are the implications of a decimal place increase? Are there sub-satoshi units on Lightning?
As mentioned starting at 4:04, watch Tadge Dryja's presentation here: https://youtu.be/-lgYYz3y_hY
There was also a paper on the topic titled "Probabilistic Lightning," written by Bloxham, Yuan ,Xia, and Jang, to which he contributed: https://courses.csail.mit.edu/6.857/2017/project/7.pdf
These are questions from the (late) April and May Patreon sessions, which took place on May 5th and May 26th 2018 respectively. If you want early-access to talks and a chance to participate in the monthly live Q&As with Andreas, become a patron: https://www.patreon.com/aantonop
RELATED:
Bitcoin, Lightning, and Streaming Money - https://youtu.be/gF_ZQ_eijPs
The Internet of Money: Five Years Later - https://youtu.be/6xIq0FdmsIA
The Lightning Network - https://www.youtube.com/playlist?list=PLPQwGV1aLnTurL4wU_y3jOhBi9rrpsYyi
Advanced Bitcoin Scripting, Part 1: Transactions and Multisig - https://youtu.be/8FeAXjkmDcQ
Advanced Bitcoin Scripting, Part 2: SegWit, Consensus, and Trustware - https://youtu.be/pQbeBduVQ4I
The Lightning Network - https://youtu.be/vPnO9ExJ50A
Lightning's security model - https://youtu.be/_GNsT_ufkec
Eltoo, and the early days of Lightning - https://youtu.be/o6eFZ5aI9N0
Misconceptions about the Lightning Network - https://youtu.be/c4TjfaLgzj4
Lightning Network scaling - https://youtu.be/4KiWkwo48k0
Lightning Network interoperability - https://youtu.be/1HYMWcJHGXc
Atomic swaps - https://youtu.be/fNFBA2UmUmg
Running nodes and payment channels - https://youtu.be/ndcfBfE_yoY
What is Segregated Witness (SegWit)? - https://youtu.be/dtOjjB4mD8k
SegWit and fork research - https://youtu.be/OorLoi01KEE
The 21 million supply cap - https://youtu.be/AABkJ55Zz3A
Divisibility and deflationary monetary policy - https://youtu.be/xhLgxX_wU6E
Inflation and debt systems - https://youtu.be/6CwxHiKf27A
HODLing and the "get free" scheme - https://youtu.be/MhOwmsW1YNI
Wealth distribution statistics - https://youtu.be/X2Qsz4eaSPY
Andreas M. Antonopoulos is a technologist and serial entrepreneur who has become one of the most well-known and respected figures in bitcoin.
Follow on Twitter: @aantonop https://twitter.com/aantonop
Website: https://antonopoulos.com/
He is the author of two books: “Mastering Bitcoin,” published by O’Reilly Media and considered the best technical guide to bitcoin; “The Internet of Money,” a book about why bitcoin matters.
THE INTERNET OF MONEY, v1: https://www.amazon.co.uk/Internet-Money-collection-Andreas-Antonopoulos/dp/1537000454/ref=asap_bc?ie=UTF8
[NEW] THE INTERNET OF MONEY, v2: https://www.amazon.com/Internet-Money-Andreas-M-Antonopoulos/dp/194791006X/ref=asap_bc?ie=UTF8
MASTERING BITCOIN: https://www.amazon.co.uk/Mastering-Bitcoin-Unlocking-Digital-Cryptocurrencies/dp/1449374042
[NEW] MASTERING BITCOIN, 2nd Edition: https://www.amazon.com/Mastering-Bitcoin-Programming-Open-Blockchain/dp/1491954388
Translations of MASTERING BITCOIN: https://bitcoinbook.info/translations-of-mastering-bitcoin/
Subscribe to the channel to learn more about Bitcoin & open blockchains!
Music: "Unbounded" by Orfan (https://www.facebook.com/Orfan/)
Outro Graphics: Phneep (http://www.phneep.com/)
Outro Art: Rock Barcellos (http://www.rockincomics.com.br/)

http://www.expersignal.com/ - ExperCharts4FX has a unique feature called "Neural Candles", which were invented by the ExperCharts4FX creator. This video shows how you can use Neural Candle's visual clues to improve your Forex Trading.
Neural Candles help reduce clutter and noise in the market to improve your timing and reduce your stress.
Visit http://www.expersignal.com/ for more information

2017 java ieee projects l-Injection: Toward Effective Collaborative Filtering Using Uninteresting Items
Abstract:
We develop a novel framework, named as l-injection, to address the sparsity problem of recommender systems. By carefully injecting low values to a selected set of unrated user-item pairs in a user-item matrix, we demonstrate that top-N recommendation accuracies of various collaborative filtering (CF) techniques can be significantly and consistently improved. We first adopt the notion of pre-use preferences of users toward a vast amount of unrated items. Using this notion, we identify uninteresting items that have not been rated yet but are likely to receive low ratings from users, and selectively impute them as low values. As our proposed approach is method-agnostic, it can be easily applied to a variety of CF algorithms. Through comprehensive experiments with three real-life datasets (e.g., Movielens, Ciao, and Watcha), we demonstrate that our solution consistently and universally enhances the accuracies of existing CF algorithms (e.g., item-based CF, SVD-based CF, and SVD++) by 2.5 to 5 times on average. Furthermore, our solution improves the running time of those CF methods by 1.2 to 2.3 times when its setting produces the best accuracy. The datasets and codes that we used in the experiments are available at: https://goo.gl/KUrmip.